How to get started with Data Science using R

R being the lingua franca of data science and is one of the popular language choices to learn data science. Once the choice is made, often beginners find themselves lost in finding out the learning path and end up with a signboard as below.

First Things First

Get Rstudio: RStudio is a leading IDE for R development. This will help you to code more productively with all the plots, package management and the editor in one place.

Become a LearneR

Take your little steps by understanding the syntax, data structures and libraries in R

In the post 5 Steps to Get Started With Data Science have provided a list of resources to learn R. These resources would be a good starting point and help you in the incremental learning. R has a strong user community with ever growing list of packages and support. Once you are comfortable with the basics, start exploring the packages for different data science tasks. Learn how to import data sets in R using packages like readr , data.table.

Understand the R community and seek help actively from Stack Overflow. Sign up and follow R-bloggers for new snippets to try out.

Data Science with R

Now that you are familiar with R, the next step is using R to solve Data Science problems. Below is a list of common data science tasks and how you could use R to achieve them.

Data Loading

Getting the data into R is the first step of the data science process. R has a wide range of options to get data of all formats into R. Below is a common list of packages best suited for data loading.

readr

data.table

XLConnect

rjson

XML

foreign

Data Analysis & Visualization

After getting the data into the R environment the next step in the data science workflow is to do simple exploratory analysis. Below are a list of wonderful R packages that helps to simplify data analysis and preparation.

dplyr Learn dplyr which helps you do simple and elegant data manipulation

Communicate Results

Now that you have some insights from the data, it is lost without effective communication. R Markdown is great tool for reporting your insights and share with fellow data scientists.

Start Small .. Build Big

Understanding algorithms, building your first recipes for common data science tasks is the small step. This is where most of the tutorials, courses and blogs stop. You could achieve this small step in a weekend and focus on the next big step by building your repository of small projects. By this you build up your skill for R and data science